基于积温的文冠果开花物候期预测模型的构建

目的建立不同地区及不同类型的文冠果物候模型,为文冠果的经营活动和旅游管理提供理论依据。方法以北京市大东流苗圃文冠果3个遗传类型:白花类型及“金冠霞帔”“匀冠锦霞”两个文冠果新品种为研究对象,于2017年进行了花期表型调查与物候的观测,结合全国文冠果主要分布区的8个省份15个地点白花文冠果初花期、盛花期、末花期的观测数据,应用中国气象数据网上共享气象数据,对花性状与3个开花物候期进行了时间和空间尺度上的分析。结果（1）3个不同花色遗传类型开花先后顺序为白花类型、“金冠霞帔”“匀冠锦霞”,物候期差异显著或极显著,花序生长随0、3、5、7、10 ℃积温的变化与Logistic生长模型拟合结果较好;花朵数随时间和积温的变化与二次多项式模型拟合较好;（2）各个地区之间同一积温指数各物候期所需积温相差不大,不同积温指数所需积温有显著性差异;不同积温指数和不同物候时期都对物候所需积温影响差异极显著,两个因素交互作用影响差异极显著;（3）5 ℃积温指数（即温暖指数）与物候期日序具有高度相关性,可用于花期预测;（4）白花类型文冠果3个物候期5 ℃积温的日序与经纬度、海拔呈极显著的多元线性回归关系,各观测地点日序的回归模拟值与观测值单因素方差分析证实该回归模型可用于花期预测;（5）用克里金插值法,采用上述预测模型,绘制白花文冠果3个开花物候期的时空分布图。结论基于5 ℃积温指数（即温暖指数）建立的积温模型可用于文冠果花期预测。 

Establishment of the flowering phenological model of Xanthoceras sorbifolium based on accumulated temperature

Objective Establishing different regions and different types of phenology model aims to provide theoretical basis for tourism and business activity management. Method We choose the three genetic types of the Dadongliu Nursery Garden in Beijing, i.e., the white flower; the new varieties as ‘Jinguanxiapei’ and ‘Junguanjinxia’ as the research objects and performed the observation of flowering phenotypes in 2017. Referring to the observed data of the early flowering stage, the full flowering stage and late flowering stage of the white yellowhorn in 15 different locations of 8 provinces in main distribution area of Xanthoceras sorbifolium, we used the sharing meteorological data from the Chinese meteorological data website to analyze floral traits and three flowering phenology periods in different space-time scales. Result (1) The flowering sequences of three different genetic types of yellowhorn, i.e., the white flower, Jinguanxiapei” and Junguanjinxia” as well as the differences in phenological period were significant or extremely significant. In addition, the changes of inflorescence growth with 0, 3, 5, 7, and 10 ℃ accumulative temperature fit the Logistic growth model better and the number of flowers changed with time and accumulative temperature fit the quadratic polynomials better. (2) The required accumulated temperature for each phenology in different locations with same accumulated temperature index showed no difference, the required accumulated temperature in different accumulated temperature index showed significant difference. However, both accumulated temperature index and phenology showed significant influence on required accumulated temperature and there was a significant difference on interaction between them. (3) The 5 ℃ cumulative temperature index (the warmth index) was highly correlated with the phenological date and could be used for flowering prediction. (4) The 5 ℃ accumulated temperature date of three flowering phenologies of white flowers showed an extremely significant multivariable regression relationship with longitude, latitude and altitude. The one-way ANOVO and simulated values at different observation sites confirmed that this regression model could be used for flowering prediction. (5) The Krisking model can be used to draw the space-time distribution maps of the three flowering phenologies of the white flower yellowhorn. Conclusion The flowering phenological model of Xanthoceras sorbifolium based on 5 ℃ cumulative temperature index (the warmth index) can be used to predict flowering period.